AI gateway and tool-connection controlsAdditional if: Model or tool traffic uses a security gateway · Calls to models, tools, or connections are routed through a security gateway, including Model Context Protocol (MCP) connections.Action-taking agent safeguardsAdditional if: AI takes multi-step actions · The employee-facing AI performs multiple steps or takes actions rather than only generating content.AI data protectionDirectly addresses · Inspect prompts, responses, and files to prevent sensitive data from moving through AI tools.
Vendor research
Wallarm AI Control Platform
Review what this vendor says publicly, the security topics those statements may support, what remains unverified, and factual company context. This is not an assessment of product effectiveness or fit.
Use-case context
How this vendor relates to the selected use case
Related research availableBack to use case →
These links show approaches associated with this vendor. The relationship label describes how the approach maps to the use case—not product effectiveness, complete requirement coverage, or fit.
Company scale
Growth stage?
Growth stageA provider with at least $25M in known funding or at least 51 employees that has not reached the scaled threshold.This is a company-scale signal, not a product-quality rating.
?
Growth stageA provider with at least $25M in known funding or at least 51 employees that has not reached the scaled threshold.This is a company-scale signal, not a product-quality rating.A descriptive band derived from retained public revenue, workforce, ownership, or funding signals.
Company scale is separate from product features, effectiveness, and suitability.
- 80+ employees
- Private-company revenue and profitability not sourced
Company context
Private independent company; no acquisition or parent-company claim was found on the reviewed Wallarm-controlled pages
Wallarm reported 80+ engineers, 134% enterprise net revenue retention, and 99% customer production deployment in April 2025; it announced general availability of the AI Control Platform on June 4, 2026
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 10
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 9
Company intelligence
Who is behind the product
Company facts provide evaluation context. Each signal is kept separate because tenure, workforce, funding, and hiring answer different questions.
FoundedNot stated on the reviewed current Wallarm-controlled company and product pages
HeadquartersNot resolved: Wallarm's current company page says Austin, Texas, while 2026 Wallarm press materials also identify San Francisco, California
OwnershipPrivate independent company; no acquisition or parent-company claim was found on the reviewed Wallarm-controlled pages
Employees80+
Capital and scaleIndependent company
Wallarm
- Known funding
- Amount not disclosed
- Operating scale
- Wallarm reported 80+ engineers, 134% enterprise net revenue retention, and 99% customer production deployment in April 2025; it announced general availability of the AI Control Platform on June 4, 2026
- Backing context
- $55M Series C led by Toba Capital; Wallarm's current company page also names Toba Capital, Y Combinator, Partech, and other investors
Series C · $55M · 2025-07-31
Named investors
Toba Capital · Y Combinator · Partech
Founders and leadership2 people listed
- Ivan NovikovCurrent role listed
Co-Founder and Board Member
- Stepan IlyinCurrent role listed
Co-Founder
Operating signalsRead each signal separately
There is no combined company rating. The company-scale label uses stated size thresholds; product features and effectiveness require separate evidence.
- Company tenure
- Not stated on the reviewed current Wallarm-controlled company and product pages
- Workforce scale
- 80+
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Open research questions
- Private-company funding total is not yet supported by a public source.
Company sources and research limits6 linked public sources
Only company facts supported by retained public sources are shown. Missing values remain unknown, and company scale does not establish product effectiveness.
Company sourceWallarm AI Control Platform documentationWallarm documents API Security, AI Hypervisor, Infrastructure Discovery, and API Security Testing as products in an AI and API security control loop.Company sourceWallarm Agentic AI Protection documentationWallarm documents AI-agent payload inspection and MCP-specific access-control, request-verification, and tool-input-schema policies with detection and blocking actions.Company sourceWallarm company pageWallarm's current company page identifies Austin as headquarters and names its investor backing; this conflicts with San Francisco references retained in some 2026 company press materials.Company sourceWallarm Series C announcementWallarm announced a $55M Series C investment led by Toba Capital on July 31, 2025; the announcement does not state total company funding.Company sourceWallarm AI Control Platform launchWallarm announced general availability of its AI Control Platform on June 4, 2026 and described runtime discovery, observation, enforcement, and governance for enterprise AI workloads.Company information sourceWallarm 2024 operating resultsSupports the company facts shown in this profile.
Solution areas
These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.
Buyer context
- Treat Wallarm as an application programming interface (API)- and runtime-rooted AI security platform, not as an SSE or workforce browser-control provider.
- Public evidence is strongest for AI and Model Context Protocol (MCP) runtime visibility, prompt and payload protection, Model Context Protocol (MCP) method and tool policy, agent-session control, AI software-bill-of-materials evidence, and application programming interface (API)-layer protection.
- Confirm deployment fit during diligence: AI Hypervisor is documented for Amazon EKS, and the professional-services red-team offering should not be represented as a self-service product capability.
Related frameworks
Where public vendor statements relate to framework requirements
11 related frameworks · expand when needed
Related frameworks
Where public vendor statements relate to framework requirements
These links show related requirements for further review. They do not establish framework compliance or control implementation. Open the full framework crosswalk →
Current referenceCSA AI Controls Matrix
- Requirements with public support
- 9
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 9
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 9
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 9
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 9
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 9
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 9
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 9
- Related requirements
- 13
- References
- 25
- Requirements with public support
- 8
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 8
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 3
- Related requirements
- 3
- References
- 3
Evaluation questions
What to verify beyond public claims
These questions come from security requirements with some public support. Use them as starting points for demonstrations, documentation review, customer references, or a buyer-observed pilot.
- 01Approved AI usage monitoring
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
- 02Approved AI usage monitoring
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
- 03Sensitive-data protection for generative AI
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
- 04Sensitive-data protection for generative AI
A policy redacts, blocks, coaches, or records the sensitive data event before it leaves the approved path.
- 05Generative AI application security
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
- 06Generative AI application security
A prompt-injection or unsafe-output test is detected, blocked, or flagged by the guardrail or large language model (LLM) firewall.
- 07AI model and supply-chain security
A test model or AI artifact appears in inventory with origin, version, hash or provenance, and deployment context.
- 08AI model and supply-chain security
A malicious, tampered, unsafe, or policy-violating artifact produces a finding before deployment.
Detailed security-requirement research18 evaluation items · supporting evidence and open research are shown separatelyExpand
Security requirementPublic supportRelated frameworksWhat to verify
Unapproved AI use discovery
No supporting claim foundDiscover and monitor workforce AI tools, accounts, prompts, domains, models, users, and usage outside approved controls.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
AI-feature discovery in business applications
No supporting claim foundInventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A software as a service (SaaS) app with an embedded AI feature appears in the software as a service (SaaS) AI inventory with app, provider, and feature context.
Approved AI usage monitoring
Limited public supportMonitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
Controls for unapproved AI use
No supporting claim foundBlock, coach, redirect, or contain non-approved AI use and policy-violating AI interactions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
Sensitive-data protection for generative AI
Limited public supportDetect, classify, redact, or block sensitive data in prompts, responses, files, retrieval, memory, and AI-connected workflows.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
Browser and business-application controls
No supporting claim foundApply session-level controls in browser and software as a service (SaaS) workflows, including uploads, downloads, copy/paste, sharing, and identity-aware access decisions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A session-level policy controls upload, download, copy, paste, sharing, or form submission in a browser or software as a service (SaaS) workflow.
Generative AI application security
Strong public supportProtect enterprise-built large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, application programming interfaces (APIs), model calls, tools, and production runtime behavior.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
AI governance, risk, and compliance
No supporting claim foundInventory AI systems and owners, translate policy and regulatory obligations into governed workflows, assess risk, manage approvals and exceptions, and retain audit evidence across the AI lifecycle.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test AI system is registered with owner, intended use, risk tier, lifecycle state, and applicable obligations.
AI assurance and adversarial testing
No supporting claim foundTest models, applications, retrieval-augmented generation (RAG) systems, and agents before release and continuously thereafter using adversarial probes, evaluation suites, attack simulation, and security release gates.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A controlled test campaign exercises an AI model, application, or agent against named AI attack classes.
AI model and supply-chain security
Limited public supportDiscover, inventory, scan, validate, and monitor models, datasets, model artifacts, registries, dependencies, and AI development assets for tampering, unsafe serialization, provenance gaps, or malicious content.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test model or AI artifact appears in inventory with origin, version, hash or provenance, and deployment context.
AI gateway, tool-connection, and runtime controls
Strong public supportMediate model, agent, tool, application programming interface (API), connector, and Model Context Protocol (MCP) traffic through an enforcement point that applies identity-aware policy, content controls, routing, rate limits, and auditable allow or deny decisions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A model, agent, tool, or Model Context Protocol (MCP) request passes through a named policy enforcement point.
Action-taking agent monitoring
Strong public supportObserve and govern agent plans, memory, tool calls, delegated tasks, autonomy, runtime decisions, and outcomes.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test agent run captures plan, steps, tool calls, outcome, and timestamps.
Agent-to-agent communication security
Limited public supportAuthorize, log, and control agent-to-agent, agent-to-tool, Model Context Protocol (MCP), connector, and tool-chain handoffs.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
An agent, tool, connector, or Model Context Protocol (MCP) handoff logs source identity, destination, and authorization decision.
Non-human identity and service-account security
No supporting claim foundInventory, least privilege, credential hygiene, monitoring, and lifecycle management for non-human identities, workloads, service accounts, application programming interface (API) keys, and machine credentials.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test service account, agent identity, or non-human identity appears in inventory with owner and privileges.
AI agent identity and permissions
Limited public supportRegister AI agents as accountable identities, bind them to owners and delegating users, authorize task- and tool-level access, issue short-lived credentials, review access, and revoke or suspend agent authority.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test agent is registered with a unique identity, accountable owner, purpose, and permitted resources.
AI coding-agent and workstation security
No supporting claim foundDiscover and govern AI coding agents, integrated development environment (IDE) assistants, command-line agents, skills, hooks, extensions, Model Context Protocol (MCP) tools, filesystem access, commands, network activity, secrets, and software-supply-chain actions on developer workstations and build environments.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test coding agent and its skills, hooks, extensions, or Model Context Protocol (MCP) tools appear in an attributable inventory.
AI cost and usage controls
Limited public supportVisibility, attribution, budgeting, rate limiting, anomaly detection, and optimization for AI usage and spend across models, agents, workflows, and owners.
Commercial MetadataNIST AI RMF PlaybookNIST Cybersecurity Framework 2.0ISO/IEC 42001OWASP Agentic AI Security Solutions Landscape
A controlled AI usage event is attributed to user, team, model, workflow, or owner with cost or token metrics.
Licensing model
No supporting claim foundPublicly discoverable commercial model such as per user, per seat, per app, per token, per integration, or enterprise platform license.
The vendor can map the sourced commercial model to per-user, per-seat, per-app, per-token, per-integration, or platform packaging.
Public sources
Vendor statements and quoted evidence
Showing the first 6 of 19 source records. Open additional records only when needed.
Wallarm AI Control Platform materials reviewed did not provide a public claim for workforce discovery of unmanaged AI tools, accounts, users, and usage.
No quoted source text is recorded for this claim.
Wallarm AI Control Platform materials reviewed did not provide a public claim for AI-feature discovery across the enterprise business-application environment.
No quoted source text is recorded for this claim.
Wallarm AI Hypervisor claims capture of outbound AI-workload connections to large language models (LLMs), application programming interfaces (APIs), databases, and third-party services.
captures every outbound connection an AI workload makes on EKS
Wallarm AI Control Platform materials reviewed did not provide a public claim for blocking, coaching, redirecting, or containing unapproved AI use.
No quoted source text is recorded for this claim.
Wallarm claims agentic AI payload protection against injection attacks and data leakage.
preventing injection attacks and data leakage
Wallarm AI Control Platform materials reviewed did not provide a public claim for session-level browser or software as a service (SaaS) controls for employee AI use.
No quoted source text is recorded for this claim.
Show 13 additional evidence records
Wallarm claims AI payload inspection policies that detect and block AI-agent attacks.
AI payload inspection mitigation controls
Wallarm AI Control Platform materials reviewed did not provide a public claim for AI inventory, risk, approval, exception, and compliance workflows.
No quoted source text is recorded for this claim.
Wallarm AI Control Platform materials reviewed did not provide a public claim for AI-specific adversarial testing and release assurance.
No quoted source text is recorded for this claim.
Wallarm AI Hypervisor claims an AI software bill of materials for observed AI workloads.
AI software bill of materials (AI-SBOM)
Wallarm claims Model Context Protocol (MCP) policies that enforce access, validate request parameters, check tool schemas, and block by IP or session.
enforce access policies, validate request parameters, and ensure tool calls conform to the published schema
Wallarm AI Hypervisor claims observation of each AI-agent decision and runtime connection.
Observes every AI agent decision
Wallarm claims access and schema controls for agent-to-Model Context Protocol (MCP) tool handoffs.
ensure tool calls conform to the published schema
Wallarm AI Control Platform materials reviewed did not provide a public claim for non-human identity and machine-credential lifecycle controls.
No quoted source text is recorded for this claim.
Wallarm AI Hypervisor claims revocation of compromised agent sessions using user identity or trace ID.
revokes compromised AI agent sessions by user identity or trace ID
Wallarm AI Control Platform materials reviewed did not provide a public claim for coding-agent, integrated development environment (IDE), CLI, workstation, tool, and package-action governance.
No quoted source text is recorded for this claim.
Wallarm claims controls for agent abuse that produces usage and credit overages.
Usage abuse and credits overages
Wallarm AI Control Platform materials reviewed did not provide a public claim for a public per-user, per-app, usage-based, or enterprise-platform commercial model.
No quoted source text is recorded for this claim.
Wallarm positions its application programming interface (API) and AI controls as external runtime protection deployable across cloud, hybrid, and edge environments.
Deploys wherever your traffic lives — cloud, hybrid, or edge.